부산대학교 도서관

Designing new molecules is an important task in the field of pharmaceuticals. Due to the vast design space of molecules, generating molecules conditioned on a specific sub-structure relevant to a particular function or therapeutic target is a crucial task in computer-aided drug design. In this paper, we present ControlMol, which adds sub-structure control to molecule generation with diffusion models. Unlike previous methods which view this task as inpainting or conditional generation, we adopt the idea of ControlNet into conditional molecule generation and make adaptive adjustments to a pre-trained diffusion model. We apply our method to both 2D and 3D molecule generation tasks. Conditioned on randomly partitioned sub-structure data, our method outperforms previous methods by generating more valid and diverse molecules. The method is easy to implement and can be quickly applied to a variety of pre-trained molecule generation models.
Comment: 9 pages,7 figures